All Data clusters (Deep learning, DEL (Deep Embedding Clustering layer))
resultft_DEL_all_5 <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/resultft_DEL_all_5_cluster.csv')
# replacing the empty space "" values with no as done in the main analysis file
resultft_DEL_all_5$farmlive[resultft_DEL_all_5$farmlive == ""] <- NA
resultft_DEL_all_5 <- resultft_DEL_all_5 %>% replace_na (list(farmlive = 'no'))
#tsne_converted_food$cl_DEL <- factor(resultft_DEL_all$cluster)
#ggplot(tsne_converted_food, aes(x=X, y=Y, color=cl_DEL)) + geom_point()
resultft_DEL_all_5$cluster <- as.factor(resultft_DEL_all_5$cluster)
DEC_Embedding_5 = read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/DEC_Embedding_5.csv')
set.seed(10)
#tsne_converted_food_DEL <- Rtsne(X = EDL_Embedding ,perplexity= 200, is_distance = FALSE, check_duplicates = FALSE)
tsne_converted_food_DEC_5 <- Rtsne(X = DEC_Embedding_5 ,perplexity= 150, is_distance = FALSE, check_duplicates = FALSE)
tsne_converted_food_DEC_5 <- tsne_converted_food_DEC_5$Y %>%
data.frame() %>%
setNames(c("X", "Y"))
tsne_converted_food_DEC_5$cl <- factor(resultft_DEL_all_5$cluster)
ggplot(tsne_converted_food_DEC_5, aes(x=X, y=Y, color=cl)) + geom_point()

tsne_converted_food_DEC_3d_5 <- Rtsne(X = DEC_Embedding_5 ,perplexity= 150, dims = 3, is_distance = FALSE, check_duplicates = FALSE)
tsne_converted_food_DEC_3d_5 <- tsne_converted_food_DEC_3d_5$Y %>%
data.frame() %>%
setNames(c("X", "Y", "Z"))
tsne_converted_food_DEC_3d_5$cl <- factor(resultft_DEL_all_5$cluster)
p <- plot_ly(tsne_converted_food_DEC_3d_5, x = ~X, y = ~Y, z = ~Z, color = ~cl, colors = c('#BF382A', '#0C4B8E')) %>%
add_markers() %>%
layout(scene = list(xaxis = list(title = 'Dim1'),
yaxis = list(title = 'Dim2'),
zaxis = list(title = 'Dim3')))
p

Density plot shoiwing the age distribution for each cluster
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all_5$cluster)
age_g <- ggplot(resultft_DEL_all_5, aes(sIgE_f3))
age_p <- age_g + geom_density(aes(fill=factor(cluster)), alpha=0.8) +
labs(title="Density plot",
subtitle="sIgE_f1 of persons Grouped by Clusters",
caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
x="sIgE_f1",
fill="# Clusters")
ggplotly(age_p)
g <- ggplot(resultft_DEL_all_5, aes(bmi2)) + scale_fill_brewer(palette = "Spectral")
s <- g + geom_histogram(aes(fill=factor(cluster)),
bins=5,
col="black",
size=.1) + # change number of bins
labs(title="Histogram with Fixed Bins",
subtitle="Age across different clusters",
x="Age",
fill="# Clusters")
ggplotly(s)
table_uft_DEL_all <- tableby(cluster ~ ., data = as.list(resultft_DEL_all_5))
summary(table_uft_DEL_all, title = "Charachtaristcs of Clusters")
Table: Charachtaristcs of Clusters
| sIgE_f1 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.004 (0.043) |
0.000 (0.000) |
0.084 (0.257) |
0.099 (0.227) |
1.074 (6.866) |
0.082 (1.747) |
|
| Range |
0.000 - 0.626 |
0.000 - 0.000 |
0.000 - 2.857 |
0.000 - 1.032 |
0.000 - 73.692 |
0.000 - 73.692 |
|
| sIgE_f2 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.001 (0.009) |
0.000 (0.000) |
0.112 (0.228) |
0.089 (0.223) |
0.699 (2.089) |
0.060 (0.558) |
|
| Range |
0.000 - 0.160 |
0.000 - 0.000 |
0.000 - 1.091 |
0.000 - 1.280 |
0.000 - 13.623 |
0.000 - 13.623 |
|
| sIgE_f3 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.004) |
0.000 (0.000) |
0.012 (0.023) |
0.104 (0.259) |
0.074 (0.186) |
0.009 (0.069) |
|
| Range |
0.000 - 0.052 |
0.000 - 0.000 |
0.000 - 0.137 |
0.000 - 1.080 |
0.000 - 1.332 |
0.000 - 1.332 |
|
| sIgE_f4 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.001 (0.006) |
0.000 (0.000) |
0.047 (0.125) |
0.193 (0.321) |
0.966 (1.890) |
0.073 (0.534) |
|
| Range |
0.000 - 0.090 |
0.000 - 0.000 |
0.000 - 1.129 |
0.000 - 1.280 |
0.000 - 12.512 |
0.000 - 12.512 |
|
| sIgE_f13 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.003 (0.019) |
0.009 (0.056) |
0.141 (0.190) |
1.104 (2.873) |
4.282 (18.801) |
0.324 (4.861) |
|
| Range |
0.000 - 0.411 |
0.000 - 0.726 |
0.000 - 1.017 |
0.008 - 16.776 |
0.000 - 149.746 |
0.000 - 149.746 |
|
| sIgE_f14 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.002) |
0.000 (0.000) |
0.011 (0.034) |
0.080 (0.140) |
0.670 (1.761) |
0.046 (0.472) |
|
| Range |
0.000 - 0.030 |
0.000 - 0.000 |
0.000 - 0.310 |
0.000 - 0.462 |
0.000 - 12.386 |
0.000 - 12.386 |
|
| sIgE_f17 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.026 (0.150) |
0.058 (0.215) |
2.554 (2.258) |
6.440 (4.758) |
20.201 (21.721) |
1.794 (7.461) |
|
| Range |
0.000 - 1.619 |
0.000 - 1.715 |
0.000 - 8.681 |
0.000 - 13.119 |
0.000 - 111.259 |
0.000 - 111.259 |
|
| sIgE_f18 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.001 (0.012) |
0.002 (0.026) |
0.036 (0.175) |
0.094 (0.398) |
0.529 (4.297) |
0.042 (1.091) |
|
| Range |
0.000 - 0.322 |
0.000 - 0.686 |
0.000 - 1.744 |
0.000 - 2.901 |
0.000 - 46.879 |
0.000 - 46.879 |
|
| sIgE_f20 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.002 (0.011) |
0.003 (0.014) |
0.085 (0.110) |
0.214 (0.225) |
1.031 (1.638) |
0.083 (0.485) |
|
| Range |
0.000 - 0.138 |
0.000 - 0.153 |
0.000 - 0.945 |
0.000 - 1.294 |
0.000 - 9.959 |
0.000 - 9.959 |
|
| sIgE_f36 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.002 (0.012) |
0.004 (0.014) |
0.053 (0.057) |
0.152 (0.206) |
0.409 (1.041) |
0.039 (0.283) |
|
| Range |
0.000 - 0.209 |
0.000 - 0.120 |
0.000 - 0.321 |
0.000 - 1.051 |
0.000 - 7.754 |
0.000 - 7.754 |
|
| gender2 |
|
|
|
|
|
|
0.005 |
| females |
423 (54.7%) |
416 (58.5%) |
97 (44.9%) |
24 (45.3%) |
62 (52.1%) |
1022 (54.6%) |
|
| males |
350 (45.3%) |
295 (41.5%) |
119 (55.1%) |
29 (54.7%) |
57 (47.9%) |
850 (45.4%) |
|
| age |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
62.134 (8.549) |
37.242 (9.728) |
46.215 (15.539) |
40.945 (13.208) |
42.437 (14.837) |
48.991 (15.551) |
|
| Range |
30.006 - 77.746 |
18.146 - 64.264 |
19.266 - 76.190 |
20.420 - 75.003 |
19.058 - 78.075 |
18.146 - 78.075 |
|
| bmi2 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
28.285 (4.765) |
24.891 (3.742) |
25.902 (3.947) |
25.977 (4.125) |
25.954 (4.534) |
26.507 (4.540) |
|
| Range |
16.975 - 50.058 |
17.404 - 41.007 |
18.290 - 41.197 |
19.493 - 37.950 |
17.915 - 38.514 |
16.975 - 50.058 |
|
| farmlive |
|
|
|
|
|
|
|
| |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
|
| no |
632 (81.8%) |
655 (92.1%) |
197 (91.2%) |
50 (94.3%) |
114 (95.8%) |
1648 (88.0%) |
|
| yes |
141 (18.2%) |
56 (7.9%) |
19 (8.8%) |
3 (5.7%) |
5 (4.2%) |
224 (12.0%) |
|
| family_allergy_hist |
|
|
|
|
|
|
< 0.001 |
| no |
566 (73.2%) |
297 (41.8%) |
106 (49.1%) |
16 (30.2%) |
37 (31.1%) |
1022 (54.6%) |
|
| yes |
207 (26.8%) |
414 (58.2%) |
110 (50.9%) |
37 (69.8%) |
82 (68.9%) |
850 (45.4%) |
|
# adding the id variable
#result_food_uft_DEL_k$ID <- food_data_id$ID
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
Charachtiristic Analysis
resultft_DEL_all_5$cluster <- as.factor(resultft_DEL_all_5$cluster)
catdes(resultft_DEL_all_5, 16)
Link between the cluster variable and the categorical variables (chi-square test)
=================================================================================
p.value df
family_allergy_hist 1.462223e-41 4
farmlive 2.177886e-10 4
gender2 5.296437e-03 4
Description of each cluster by the categories
=============================================
$`0`
Cla/Mod Mod/Cla Global p.value v.test
family_allergy_hist=no 55.38160 73.22122 54.59402 5.241366e-43 13.747922
farmlive=yes 62.94643 18.24062 11.96581 3.836394e-12 6.943081
farmlive=no 38.34951 81.75938 88.03419 3.836394e-12 -6.943081
family_allergy_hist=yes 24.35294 26.77878 45.40598 5.241366e-43 -13.747922
$`1`
Cla/Mod Mod/Cla Global p.value v.test
family_allergy_hist=yes 48.70588 58.227848 45.40598 2.776694e-18 8.720217
farmlive=no 39.74515 92.123769 88.03419 1.296541e-05 4.360688
gender2=females 40.70450 58.509142 54.59402 7.755781e-03 2.662521
gender2=males 34.70588 41.490858 45.40598 7.755781e-03 -2.662521
farmlive=yes 25.00000 7.876231 11.96581 1.296541e-05 -4.360688
family_allergy_hist=no 29.06067 41.772152 54.59402 2.776694e-18 -8.720217
$`2`
Cla/Mod Mod/Cla Global p.value v.test
gender2=males 14.000000 55.09259 45.40598 0.002480136 3.025754
gender2=females 9.491194 44.90741 54.59402 0.002480136 -3.025754
$`3`
Cla/Mod Mod/Cla Global p.value v.test
family_allergy_hist=yes 4.352941 69.81132 45.40598 0.0003159048 3.601896
family_allergy_hist=no 1.565558 30.18868 54.59402 0.0003159048 -3.601896
$`4`
Cla/Mod Mod/Cla Global p.value v.test
family_allergy_hist=yes 9.647059 68.907563 45.40598 1.046986e-07 5.318374
farmlive=no 6.917476 95.798319 88.03419 3.155607e-03 2.952159
farmlive=yes 2.232143 4.201681 11.96581 3.155607e-03 -2.952159
family_allergy_hist=no 3.620352 31.092437 54.59402 1.046986e-07 -5.318374
Link between the cluster variable and the quantitative variables
================================================================
Eta2 P-value
age 0.53459417 4.168803e-308
sIgE_f17 0.44312635 1.912237e-235
sIgE_f20 0.26721574 2.259101e-124
sIgE_f4 0.19429220 4.761183e-86
sIgE_f36 0.12646074 1.831810e-53
sIgE_f3 0.12461333 1.297269e-52
sIgE_f14 0.11938658 3.221263e-50
bmi2 0.11488752 3.609795e-48
sIgE_f2 0.09332546 1.682564e-38
sIgE_f13 0.04646742 2.274499e-18
sIgE_f1 0.02219638 1.724935e-08
sIgE_f18 0.01388032 3.006808e-05
Description of each cluster by quantitative variables
=====================================================
$`0`
v.test Mean in category Overall mean sd in category Overall sd p.value
age 30.668195 6.213375e+01 48.990817991 8.543407112 15.54647228 1.511905e-206
bmi2 14.206408 2.828456e+01 26.507217851 4.762205738 4.53851342 8.360568e-46
sIgE_f13 -2.400902 2.783943e-03 0.324389665 0.019354127 4.85933840 1.635472e-02
sIgE_f14 -3.537504 1.723239e-04 0.046205234 0.001900249 0.47206221 4.039274e-04
sIgE_f2 -3.857459 8.040704e-04 0.060153551 0.009378138 0.55814025 1.145717e-04
sIgE_f3 -4.688430 3.528329e-04 0.009219938 0.003741759 0.06860917 2.753086e-06
sIgE_f36 -4.715205 1.980466e-03 0.038784940 0.011657685 0.28315734 2.414673e-06
sIgE_f4 -4.889479 5.302067e-04 0.072516311 0.005758889 0.53408927 1.011030e-06
sIgE_f20 -6.109043 1.752470e-03 0.083349557 0.010506697 0.48453963 1.002306e-09
sIgE_f17 -8.598199 2.595266e-02 1.793797158 0.149831424 7.45872089 8.097668e-18
$`1`
v.test Mean in category Overall mean sd in category Overall sd p.value
sIgE_f13 -2.195853 0.009161681 0.324389665 0.05561821 4.85933840 2.810249e-02
sIgE_f14 -3.313199 0.000000000 0.046205234 0.00000000 0.47206221 9.223532e-04
sIgE_f2 -3.648157 0.000000000 0.060153551 0.00000000 0.55814025 2.641283e-04
sIgE_f36 -4.167325 0.003924759 0.038784940 0.01380475 0.28315734 3.081945e-05
sIgE_f3 -4.548848 0.000000000 0.009219938 0.00000000 0.06860917 5.394044e-06
sIgE_f4 -4.595973 0.000000000 0.072516311 0.00000000 0.53408927 4.307352e-06
sIgE_f20 -5.589043 0.003345652 0.083349557 0.01379930 0.48453963 2.283241e-08
sIgE_f17 -7.878923 0.057693637 1.793797158 0.21479644 7.45872089 3.302154e-15
bmi2 -12.056213 24.890743000 26.507217851 3.73894961 4.53851342 1.798648e-33
age -25.581075 37.241975892 48.990817991 9.72107748 15.54647228 2.477828e-144
$`2`
v.test Mean in category Overall mean sd in category Overall sd p.value
bmi2 -2.082651 25.90216 26.50722 3.937503 4.538513 0.037283043
age -2.789666 46.21462 48.99082 15.503033 15.546472 0.005276239
$`3`
v.test Mean in category Overall mean sd in category Overall sd p.value
sIgE_f3 10.194439 0.1039498 0.009219938 0.2568479 0.06860917 2.099535e-24
sIgE_f17 4.599242 6.4399356 1.793797158 4.7126062 7.45872089 4.240308e-06
sIgE_f36 2.949079 0.1518832 0.038784940 0.2040307 0.28315734 3.187226e-03
sIgE_f20 1.998084 0.2144743 0.083349557 0.2228088 0.48453963 4.570757e-02
age -3.821272 40.9447943 48.990817991 13.0824425 15.54647228 1.327652e-04
$`4`
v.test Mean in category Overall mean sd in category Overall sd p.value
sIgE_f17 27.813053 20.20124627 1.793797158 21.629315 7.45872089 3.015940e-170
sIgE_f20 22.037930 1.03085274 0.083349557 1.631123 0.48453963 1.247109e-107
sIgE_f4 18.862142 0.96640914 0.072516311 1.881997 0.53408927 2.335618e-79
sIgE_f14 14.894169 0.67007808 0.046205234 1.753952 0.47206221 3.596293e-50
sIgE_f36 14.751227 0.40941150 0.038784940 1.036971 0.28315734 3.021231e-49
sIgE_f2 12.892064 0.69863218 0.060153551 2.080167 0.55814025 4.989076e-38
sIgE_f3 10.637942 0.07398196 0.009219938 0.185565 0.06860917 1.984654e-26
sIgE_f13 9.178780 4.28208805 0.324389665 18.721848 4.85933840 4.360025e-20
sIgE_f1 6.401634 1.07441269 0.082481475 6.837547 1.74626497 1.537229e-10
sIgE_f18 5.043438 0.52941789 0.041507056 4.279045 1.09026788 4.572417e-07
age -4.751317 42.43651669 48.990817991 14.774762 15.54647228 2.020965e-06
Random Data clusters with DEL (Deep Embedding Clustering layer)
#result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_food_uft_DEL_k.csv")
result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_uft_DEL_k.csv")
result_rand_uft_DEL_k$farmlive[result_rand_uft_DEL_k$farmlive == ""] <- NA
result_rand_uft_DEL_k <- result_rand_uft_DEL_k %>% replace_na (list(farmlive = 'no'))
table_rand_uft_DEL_k <- tableby(cluster ~ ., data = as.list(result_rand_uft_DEL_k))
summary(table_rand_uft_DEL_k, title = "Charachtaristcs of Clusters")
Table: Charachtaristcs of Clusters
| sIgE_f1 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.083 (0.196) |
0.000 (0.000) |
0.191 (0.540) |
0.083 (0.189) |
0.000 (0.000) |
0.016 (0.117) |
|
| Range |
0.000 - 1.032 |
0.000 - 0.000 |
0.000 - 2.845 |
0.000 - 0.990 |
0.000 - 0.000 |
0.000 - 2.845 |
|
| sIgE_f2 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.113 (0.403) |
0.000 (0.000) |
0.579 (1.506) |
0.109 (0.225) |
0.000 (0.000) |
0.030 (0.277) |
|
| Range |
0.000 - 2.750 |
0.000 - 0.000 |
0.000 - 6.849 |
0.000 - 1.091 |
0.000 - 0.000 |
0.000 - 6.849 |
|
| sIgE_f3 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.021 (0.038) |
0.000 (0.000) |
0.043 (0.062) |
0.012 (0.020) |
0.000 (0.000) |
0.003 (0.016) |
|
| Range |
0.000 - 0.213 |
0.000 - 0.000 |
0.000 - 0.211 |
0.000 - 0.080 |
0.000 - 0.000 |
0.000 - 0.213 |
|
| sIgE_f4 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.281 (0.581) |
0.000 (0.000) |
0.773 (1.718) |
0.036 (0.076) |
0.000 (0.000) |
0.035 (0.324) |
|
| Range |
0.000 - 2.543 |
0.000 - 0.000 |
0.000 - 8.417 |
0.000 - 0.428 |
0.000 - 0.000 |
0.000 - 8.417 |
|
| sIgE_f13 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.517 (0.548) |
0.002 (0.030) |
5.907 (25.114) |
0.130 (0.190) |
0.002 (0.020) |
0.187 (4.042) |
|
| Range |
0.013 - 1.957 |
0.000 - 0.642 |
0.000 - 133.659 |
0.000 - 1.017 |
0.000 - 0.411 |
0.000 - 133.659 |
|
| sIgE_f14 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.112 (0.215) |
0.000 (0.000) |
0.744 (1.782) |
0.008 (0.020) |
0.000 (0.000) |
0.025 (0.306) |
|
| Range |
0.000 - 0.833 |
0.000 - 0.000 |
0.000 - 8.505 |
0.000 - 0.160 |
0.000 - 0.000 |
0.000 - 8.505 |
|
| sIgE_f17 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
6.966 (5.245) |
0.013 (0.086) |
27.282 (21.898) |
1.724 (1.641) |
0.010 (0.082) |
1.174 (5.760) |
|
| Range |
0.000 - 20.156 |
0.000 - 1.014 |
0.000 - 76.467 |
0.000 - 6.215 |
0.000 - 1.011 |
0.000 - 76.467 |
|
| sIgE_f18 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.103 (0.415) |
0.002 (0.031) |
1.830 (8.835) |
0.029 (0.165) |
0.001 (0.015) |
0.055 (1.416) |
|
| Range |
0.000 - 2.901 |
0.000 - 0.686 |
0.000 - 46.879 |
0.000 - 1.678 |
0.000 - 0.322 |
0.000 - 46.879 |
|
| sIgE_f20 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.301 (0.286) |
0.001 (0.004) |
1.251 (1.827) |
0.066 (0.077) |
0.001 (0.005) |
0.052 (0.357) |
|
| Range |
0.000 - 1.294 |
0.000 - 0.041 |
0.000 - 9.959 |
0.000 - 0.527 |
0.000 - 0.057 |
0.000 - 9.959 |
|
| sIgE_f36 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.174 (0.222) |
0.001 (0.006) |
0.512 (1.466) |
0.053 (0.054) |
0.001 (0.007) |
0.027 (0.250) |
|
| Range |
0.000 - 1.051 |
0.000 - 0.056 |
0.000 - 7.754 |
0.000 - 0.238 |
0.000 - 0.067 |
0.000 - 7.754 |
|
| gender2 |
|
|
|
|
|
|
< 0.001 |
| females |
21 (42.9%) |
317 (65.8%) |
14 (50.0%) |
48 (46.2%) |
183 (41.6%) |
583 (52.9%) |
|
| males |
28 (57.1%) |
165 (34.2%) |
14 (50.0%) |
56 (53.8%) |
257 (58.4%) |
520 (47.1%) |
|
| age |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
43.733 (13.832) |
40.954 (12.001) |
39.981 (14.744) |
48.435 (15.603) |
62.643 (9.183) |
50.410 (15.390) |
|
| Range |
20.867 - 77.130 |
18.146 - 73.475 |
21.217 - 70.735 |
19.266 - 75.949 |
26.511 - 77.746 |
18.146 - 77.746 |
|
| bmi2 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
26.104 (4.237) |
24.543 (3.447) |
25.762 (4.977) |
26.526 (4.329) |
27.743 (4.054) |
26.107 (4.127) |
|
| Range |
20.069 - 37.950 |
17.404 - 42.768 |
18.939 - 37.545 |
19.223 - 41.007 |
16.975 - 44.816 |
16.975 - 44.816 |
|
| farmlive |
|
|
|
|
|
|
|
| |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
|
| no |
46 (93.9%) |
444 (92.1%) |
28 (100.0%) |
99 (95.2%) |
343 (78.0%) |
960 (87.0%) |
|
| yes |
3 (6.1%) |
38 (7.9%) |
0 (0.0%) |
5 (4.8%) |
97 (22.0%) |
143 (13.0%) |
|
| family_allergy_hist |
|
|
|
|
|
|
< 0.001 |
| no |
19 (38.8%) |
223 (46.3%) |
6 (21.4%) |
62 (59.6%) |
385 (87.5%) |
695 (63.0%) |
|
| yes |
30 (61.2%) |
259 (53.7%) |
22 (78.6%) |
42 (40.4%) |
55 (12.5%) |
408 (37.0%) |
|
# adding the id variable
#result_food_uft_DEL_k$ID <- food_data_id$ID
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
Charachtiristic Analysis
result_rand_uft_DEL_k$cluster <- as.factor(result_rand_uft_DEL_k$cluster)
#result_food_uft_DEL_k <- result_food_uft_DEL_k[-c(1,2,20)]
catdes(result_rand_uft_DEL_k, 16)
Link between the cluster variable and the categorical variables (chi-square test)
=================================================================================
p.value df
family_allergy_hist 3.424012e-43 4
gender2 5.745123e-12 4
farmlive 2.530325e-11 4
Description of each cluster by the categories
=============================================
$`0`
Cla/Mod Mod/Cla Global p.value v.test
family_allergy_hist=yes 7.352941 61.22449 36.99003 0.0004907976 3.485729
family_allergy_hist=no 2.733813 38.77551 63.00997 0.0004907976 -3.485729
$`1`
Cla/Mod Mod/Cla Global p.value v.test
family_allergy_hist=yes 63.48039 53.734440 36.99003 2.903413e-24 10.162890
gender2=females 54.37393 65.767635 52.85585 2.998922e-14 7.598366
farmlive=no 46.25000 92.116183 87.03536 6.721713e-06 4.502315
farmlive=yes 26.57343 7.883817 12.96464 6.721713e-06 -4.502315
gender2=males 31.73077 34.232365 47.14415 2.998922e-14 -7.598366
family_allergy_hist=no 32.08633 46.265560 63.00997 2.903413e-24 -10.162890
$`2`
Cla/Mod Mod/Cla Global p.value v.test
family_allergy_hist=yes 5.3921569 78.57143 36.99003 7.660114e-06 4.474470
farmlive=no 2.9166667 100.00000 87.03536 1.944940e-02 2.336804
farmlive=yes 0.0000000 0.00000 12.96464 1.944940e-02 -2.336804
family_allergy_hist=no 0.8633094 21.42857 63.00997 7.660114e-06 -4.474470
$`3`
Cla/Mod Mod/Cla Global p.value v.test
farmlive=no 10.312500 95.192308 87.03536 0.004857209 2.816354
farmlive=yes 3.496503 4.807692 12.96464 0.004857209 -2.816354
$`4`
Cla/Mod Mod/Cla Global p.value v.test
family_allergy_hist=no 55.39568 87.50000 63.00997 2.020566e-46 14.305542
farmlive=yes 67.83217 22.04545 12.96464 5.258224e-13 7.218453
gender2=males 49.42308 58.40909 47.14415 1.026148e-09 6.105289
gender2=females 31.38937 41.59091 52.85585 1.026148e-09 -6.105289
farmlive=no 35.72917 77.95455 87.03536 5.258224e-13 -7.218453
family_allergy_hist=yes 13.48039 12.50000 36.99003 2.020566e-46 -14.305542
Link between the cluster variable and the quantitative variables
================================================================
Eta2 P-value
sIgE_f17 0.60196868 7.499421e-218
age 0.43899232 3.695744e-136
sIgE_f20 0.32563794 2.075189e-92
sIgE_f3 0.26464644 7.440968e-72
sIgE_f4 0.16694806 2.605188e-42
sIgE_f14 0.14940898 2.167347e-37
bmi2 0.12669886 3.528532e-31
sIgE_f2 0.12139732 9.383073e-30
sIgE_f36 0.12067931 1.460744e-29
sIgE_f1 0.11707576 1.338788e-28
sIgE_f13 0.05296825 3.179828e-12
sIgE_f18 0.04120260 2.194434e-09
Description of each cluster by quantitative variables
=====================================================
$`0`
v.test Mean in category Overall mean sd in category Overall sd p.value
sIgE_f3 7.815142 0.02089372 0.003145542 0.03776886 0.01625494 5.490118e-15
sIgE_f17 7.200998 6.96621397 1.174252032 5.19134622 5.75707008 5.977324e-13
sIgE_f4 5.428826 0.28128416 0.035469421 0.57498788 0.32409368 5.672608e-08
sIgE_f20 4.990445 0.30061171 0.051857938 0.28312616 0.35677871 6.024033e-07
sIgE_f36 4.221718 0.17415762 0.026534870 0.22019235 0.25028371 2.424472e-05
sIgE_f1 4.067095 0.08298353 0.016341103 0.19364583 0.11728300 4.760291e-05
sIgE_f2 2.142614 0.11283843 0.029970671 0.39932637 0.27682810 3.214410e-02
sIgE_f14 2.053939 0.11248115 0.024610767 0.21325191 0.30621284 3.998155e-02
age -3.106838 43.73298767 50.409945023 13.68964100 15.38255887 1.891003e-03
$`1`
v.test Mean in category Overall mean sd in category Overall sd p.value
sIgE_f14 -2.350554 0.000000e+00 0.024610767 0.000000000 0.30621284 1.874548e-02
sIgE_f36 -2.977528 1.053667e-03 0.026534870 0.006484391 0.25028371 2.905830e-03
sIgE_f2 -3.166320 0.000000e+00 0.029970671 0.000000000 0.27682810 1.543809e-03
sIgE_f4 -3.200752 0.000000e+00 0.035469421 0.000000000 0.32409368 1.370694e-03
sIgE_f1 -4.074880 0.000000e+00 0.016341103 0.000000000 0.11728300 4.603809e-05
sIgE_f20 -4.202133 5.953897e-04 0.051857938 0.004200290 0.35677871 2.644118e-05
sIgE_f3 -5.659508 0.000000e+00 0.003145542 0.000000000 0.01625494 1.518077e-08
sIgE_f17 -5.898697 1.309996e-02 1.174252032 0.085751671 5.75707008 3.663829e-09
bmi2 -11.086278 2.454294e+01 26.106734104 3.443842165 4.12537389 1.462471e-28
age -17.978175 4.095398e+01 50.409945023 11.988056542 15.38255887 2.888443e-72
$`2`
v.test Mean in category Overall mean sd in category Overall sd p.value
sIgE_f17 24.295775 27.28183602 1.174252032 21.50318294 5.75707008 2.172658e-130
sIgE_f20 18.004145 1.25082112 0.051857938 1.79409828 0.35677871 1.807698e-72
sIgE_f3 13.145783 0.04303023 0.003145542 0.06050838 0.01625494 1.799323e-39
sIgE_f14 12.584599 0.74388945 0.024610767 1.74950012 0.30621284 2.566440e-36
sIgE_f4 12.188176 0.77276852 0.035469421 1.68728552 0.32409368 3.593742e-34
sIgE_f2 10.632950 0.57938296 0.029970671 1.47859712 0.27682810 2.093828e-26
sIgE_f36 10.391927 0.51200563 0.026534870 1.43978253 0.25028371 2.698459e-25
sIgE_f1 7.971785 0.19085300 0.016341103 0.53057583 0.11728300 1.563993e-15
sIgE_f13 7.586510 5.90737712 0.186718716 24.66160243 4.03989038 3.286379e-14
sIgE_f18 6.718500 1.83000093 0.054806598 8.67591954 1.41559548 1.836048e-11
age -3.632354 39.98074171 50.409945023 14.47841225 15.38255887 2.808478e-04
$`3`
v.test Mean in category Overall mean sd in category Overall sd p.value
sIgE_f1 6.071957 0.08282846 0.016341103 0.1880077 0.11728300 1.263605e-09
sIgE_f3 5.789448 0.01193168 0.003145542 0.0200712 0.01625494 7.061804e-09
sIgE_f2 3.046537 0.10871004 0.029970671 0.2244035 0.27682810 2.314938e-03
$`4`
v.test Mean in category Overall mean sd in category Overall sd p.value
age 21.505709 6.264264e+01 50.409945023 9.172829372 15.38255887 1.376644e-102
bmi2 10.726381 2.774301e+01 26.106734104 4.049642119 4.12537389 7.653490e-27
sIgE_f14 -2.173512 0.000000e+00 0.024610767 0.000000000 0.30621284 2.974177e-02
sIgE_f36 -2.763447 9.594175e-04 0.026534870 0.006813830 0.25028371 5.719440e-03
sIgE_f2 -2.927835 0.000000e+00 0.029970671 0.000000000 0.27682810 3.413307e-03
sIgE_f4 -2.959674 0.000000e+00 0.035469421 0.000000000 0.32409368 3.079646e-03
sIgE_f1 -3.767963 0.000000e+00 0.016341103 0.000000000 0.11728300 1.645848e-04
sIgE_f20 -3.876671 7.136078e-04 0.051857938 0.005353473 0.35677871 1.058954e-04
sIgE_f3 -5.233238 0.000000e+00 0.003145542 0.000000000 0.01625494 1.665657e-07
sIgE_f17 -5.469448 9.898991e-03 1.174252032 0.081973061 5.75707008 4.514381e-08
With asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)
result_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_as_rh_uft_DEL.csv')
result_as_rh_uft_DEL$farmlive[result_as_rh_uft_DEL$farmlive == ""] <- NA
result_as_rh_uft_DEL <- result_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_as_rh_uft_DEL))
summary(table_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")
Table: Charachtaristcs of Clusters
| sIgE_f1 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.117 (0.622) |
3.788 (15.346) |
0.002 (0.028) |
0.596 (1.096) |
0.151 (0.852) |
0.136 (2.300) |
|
| Range |
0.000 - 8.179 |
0.000 - 73.692 |
0.000 - 0.626 |
0.020 - 4.695 |
0.000 - 7.725 |
0.000 - 73.692 |
|
| sIgE_f2 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.078 (0.202) |
1.035 (3.295) |
0.000 (0.006) |
2.251 (2.998) |
0.072 (0.208) |
0.094 (0.728) |
|
| Range |
0.000 - 1.549 |
0.000 - 13.623 |
0.000 - 0.140 |
0.034 - 13.006 |
0.000 - 1.338 |
0.000 - 13.623 |
|
| sIgE_f3 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.013 (0.028) |
0.178 (0.391) |
0.000 (0.003) |
0.255 (0.357) |
0.026 (0.064) |
0.015 (0.090) |
|
| Range |
0.000 - 0.213 |
0.000 - 1.332 |
0.000 - 0.042 |
0.015 - 1.080 |
0.000 - 0.310 |
0.000 - 1.332 |
|
| sIgE_f4 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.112 (0.319) |
1.773 (3.369) |
0.001 (0.009) |
0.319 (0.370) |
0.497 (1.310) |
0.112 (0.687) |
|
| Range |
0.000 - 2.275 |
0.000 - 12.512 |
0.000 - 0.224 |
0.030 - 1.291 |
0.000 - 7.459 |
0.000 - 12.512 |
|
| sIgE_f13 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.302 (0.866) |
8.824 (27.898) |
0.016 (0.079) |
1.515 (3.750) |
1.329 (5.285) |
0.411 (4.497) |
|
| Range |
0.000 - 11.866 |
0.000 - 133.659 |
0.000 - 0.830 |
0.021 - 16.776 |
0.000 - 45.700 |
0.000 - 133.659 |
|
| sIgE_f14 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.048 (0.158) |
1.759 (3.429) |
0.000 (0.001) |
0.225 (0.593) |
0.216 (0.729) |
0.071 (0.601) |
|
| Range |
0.000 - 1.184 |
0.000 - 12.386 |
0.000 - 0.020 |
0.000 - 2.785 |
0.000 - 5.216 |
0.000 - 12.386 |
|
| sIgE_f17 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
4.338 (4.800) |
44.076 (35.504) |
0.077 (0.243) |
1.691 (4.311) |
10.156 (12.891) |
2.937 (9.526) |
|
| Range |
0.000 - 23.778 |
0.000 - 111.259 |
0.000 - 1.619 |
0.000 - 17.146 |
0.000 - 39.910 |
0.000 - 111.259 |
|
| sIgE_f18 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.041 (0.212) |
2.301 (9.729) |
0.005 (0.071) |
0.040 (0.071) |
0.069 (0.271) |
0.069 (1.436) |
|
| Range |
0.000 - 2.901 |
0.000 - 46.879 |
0.000 - 1.678 |
0.000 - 0.244 |
0.000 - 1.770 |
0.000 - 46.879 |
|
| sIgE_f20 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.173 (0.337) |
1.825 (2.624) |
0.006 (0.027) |
0.214 (0.401) |
0.531 (1.317) |
0.133 (0.625) |
|
| Range |
0.000 - 3.469 |
0.000 - 9.959 |
0.000 - 0.343 |
0.000 - 1.686 |
0.000 - 8.781 |
0.000 - 9.959 |
|
| sIgE_f36 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
0.086 (0.155) |
0.901 (1.941) |
0.007 (0.026) |
0.067 (0.120) |
0.175 (0.660) |
0.061 (0.368) |
|
| Range |
0.000 - 1.051 |
0.000 - 7.754 |
0.000 - 0.277 |
0.000 - 0.430 |
0.000 - 5.642 |
0.000 - 7.754 |
|
| gender2 |
|
|
|
|
|
|
0.024 |
| females |
149 (53.4%) |
14 (60.9%) |
396 (59.1%) |
7 (31.8%) |
40 (47.6%) |
606 (56.2%) |
|
| males |
130 (46.6%) |
9 (39.1%) |
274 (40.9%) |
15 (68.2%) |
44 (52.4%) |
472 (43.8%) |
|
| age |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
44.408 (14.931) |
39.107 (15.451) |
49.341 (15.383) |
51.708 (18.292) |
45.759 (14.349) |
47.615 (15.443) |
|
| Range |
19.058 - 77.130 |
20.741 - 74.510 |
19.535 - 76.656 |
21.113 - 76.190 |
19.415 - 78.075 |
19.058 - 78.075 |
|
| bmi2 |
|
|
|
|
|
|
< 0.001 |
| Mean (SD) |
26.516 (4.575) |
30.321 (10.071) |
25.964 (3.511) |
29.757 (4.191) |
33.215 (6.604) |
26.842 (4.790) |
|
| Range |
17.915 - 38.725 |
19.044 - 50.058 |
17.404 - 34.475 |
20.809 - 37.545 |
19.841 - 44.413 |
17.404 - 50.058 |
|
| farmlive |
|
|
|
|
|
|
|
| |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
|
| no |
254 (91.0%) |
23 (100.0%) |
599 (89.4%) |
20 (90.9%) |
72 (85.7%) |
968 (89.8%) |
|
| yes |
25 (9.0%) |
0 (0.0%) |
71 (10.6%) |
2 (9.1%) |
12 (14.3%) |
110 (10.2%) |
|
| family_allergy_hist |
|
|
|
|
|
|
0.006 |
| no |
106 (38.0%) |
10 (43.5%) |
324 (48.4%) |
8 (36.4%) |
27 (32.1%) |
475 (44.1%) |
|
| yes |
173 (62.0%) |
13 (56.5%) |
346 (51.6%) |
14 (63.6%) |
57 (67.9%) |
603 (55.9%) |
|
# adding the id variable
#result_food_uft_DEL_k$ID <- food_data_id$ID
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
Charachtiristic Analysis
result_as_rh_uft_DEL$cluster <- as.factor(result_as_rh_uft_DEL$cluster)
catdes(result_as_rh_uft_DEL, 16)
Without asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)
result_no_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_no_as_rh_uft_DEL.csv')
result_no_as_rh_uft_DEL$farmlive[result_no_as_rh_uft_DEL$farmlive == ""] <- NA
result_no_as_rh_uft_DEL <- result_no_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_no_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_no_as_rh_uft_DEL))
summary(table_no_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")
Table: Charachtaristcs of Clusters
| sIgE_f1 |
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.000) |
0.048 (0.141) |
0.010 (0.030) |
0.010 (0.064) |
|
| Range |
0.000 - 0.000 |
0.000 - 0.918 |
0.000 - 0.149 |
0.000 - 0.918 |
|
| sIgE_f2 |
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.000) |
0.054 (0.159) |
0.066 (0.316) |
0.014 (0.106) |
|
| Range |
0.000 - 0.000 |
0.000 - 1.091 |
0.000 - 2.143 |
0.000 - 2.143 |
|
| sIgE_f3 |
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.000) |
0.008 (0.018) |
0.008 (0.026) |
0.002 (0.011) |
|
| Range |
0.000 - 0.000 |
0.000 - 0.067 |
0.000 - 0.134 |
0.000 - 0.134 |
|
| sIgE_f4 |
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.000) |
0.042 (0.145) |
0.174 (0.593) |
0.019 (0.164) |
|
| Range |
0.000 - 0.000 |
0.000 - 1.129 |
0.000 - 2.543 |
0.000 - 2.543 |
|
| sIgE_f13 |
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.000) |
0.061 (0.185) |
3.170 (21.379) |
0.207 (5.315) |
|
| Range |
0.000 - 0.000 |
0.000 - 1.596 |
0.000 - 149.746 |
0.000 - 149.746 |
|
| sIgE_f14 |
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.000) |
0.012 (0.051) |
0.156 (0.731) |
0.012 (0.185) |
|
| Range |
0.000 - 0.000 |
0.000 - 0.366 |
0.000 - 4.886 |
0.000 - 4.886 |
|
| sIgE_f17 |
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.000) |
0.465 (1.241) |
2.520 (7.328) |
0.242 (1.977) |
|
| Range |
0.000 - 0.000 |
0.000 - 7.065 |
0.000 - 36.193 |
0.000 - 36.193 |
|
| sIgE_f18 |
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.000) |
0.010 (0.037) |
0.036 (0.140) |
0.004 (0.039) |
|
| Range |
0.000 - 0.000 |
0.000 - 0.322 |
0.000 - 0.868 |
0.000 - 0.868 |
|
| sIgE_f20 |
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.000) |
0.036 (0.099) |
0.141 (0.444) |
0.015 (0.122) |
|
| Range |
0.000 - 0.000 |
0.000 - 0.606 |
0.000 - 2.149 |
0.000 - 2.149 |
|
| sIgE_f36 |
|
|
|
|
< 0.001 |
| Mean (SD) |
0.000 (0.000) |
0.027 (0.073) |
0.058 (0.200) |
0.009 (0.061) |
|
| Range |
0.000 - 0.000 |
0.000 - 0.406 |
0.000 - 1.081 |
0.000 - 1.081 |
|
| gender2 |
|
|
|
|
0.262 |
| females |
303 (50.8%) |
84 (56.8%) |
29 (59.2%) |
416 (52.4%) |
|
| males |
294 (49.2%) |
64 (43.2%) |
20 (40.8%) |
378 (47.6%) |
|
| age |
|
|
|
|
0.920 |
| Mean (SD) |
50.736 (15.755) |
51.145 (14.937) |
51.481 (14.445) |
50.858 (15.512) |
|
| Range |
18.146 - 77.746 |
19.302 - 76.877 |
22.091 - 74.524 |
18.146 - 77.746 |
|
| bmi2 |
|
|
|
|
< 0.001 |
| Mean (SD) |
24.713 (2.715) |
28.662 (3.831) |
34.498 (5.473) |
26.053 (4.136) |
|
| Range |
16.975 - 30.653 |
18.904 - 41.197 |
22.097 - 46.094 |
16.975 - 46.094 |
|
| farmlive |
|
|
|
|
|
| |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
0 (0.0%) |
|
| no |
504 (84.4%) |
132 (89.2%) |
44 (89.8%) |
680 (85.6%) |
|
| yes |
93 (15.6%) |
16 (10.8%) |
5 (10.2%) |
114 (14.4%) |
|
| family_allergy_hist |
|
|
|
|
0.089 |
| no |
419 (70.2%) |
101 (68.2%) |
27 (55.1%) |
547 (68.9%) |
|
| yes |
178 (29.8%) |
47 (31.8%) |
22 (44.9%) |
247 (31.1%) |
|
# adding the id variable
#result_food_uft_DEL_k$ID <- food_data_id$ID
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
Charachtiristic Analysis
result_no_as_rh_uft_DEL$cluster <- as.factor(result_no_as_rh_uft_DEL$cluster)
catdes(result_no_as_rh_uft_DEL, 16)
adding the id varaible
#resultft_DEL_all$ID <- food_data_id$ID
resultft_DEL_all_5$ID <- food_data_id$ID
result_rand_uft_DEL_k$ID <- rand_food_data_id$ID
result_as_rh_uft_DEL$ID <- as_ri_food_id$ID
result_no_as_rh_uft_DEL$ID <- no_as_ri_food_id$ID
write.csv(resultft_DEL_all,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/resultft_DEL_all_id_5.csv')
write.csv(result_rand_uft_DEL_k,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_rand_uft_DEL_k_id.csv')
write.csv(result_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_as_rh_uft_DEL_id.csv')
write.csv(result_no_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_no_as_rh_uft_DEL_id.csv')
---
title: "Results food data clustering DEL- 25/May/2020"
output:
  html_notebook: default
  pdf_document: default
---

```{r loadlib, include=FALSE}
library(FactoMineR)
library(factoextra)
library(arsenal)
library(Rtsne)
library(plotly)
library(tidyverse)
```

# All Data clusters (Deep learning, DEL (Deep Embedding Clustering layer))

```{r}
resultft_DEL_all_5 <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/resultft_DEL_all_5_cluster.csv')
# replacing the empty space "" values with no as done in the main analysis file
resultft_DEL_all_5$farmlive[resultft_DEL_all_5$farmlive == ""] <- NA
resultft_DEL_all_5 <-  resultft_DEL_all_5 %>% replace_na (list(farmlive = 'no'))
#tsne_converted_food$cl_DEL <- factor(resultft_DEL_all$cluster)
#ggplot(tsne_converted_food, aes(x=X, y=Y, color=cl_DEL)) + geom_point()
resultft_DEL_all_5$cluster <- as.factor(resultft_DEL_all_5$cluster)
```

```{r}
DEC_Embedding_5 = read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/DEC_Embedding_5.csv')
```

```{r}
set.seed(10)
#tsne_converted_food_DEL <- Rtsne(X = EDL_Embedding ,perplexity= 200, is_distance = FALSE, check_duplicates = FALSE)
tsne_converted_food_DEC_5 <- Rtsne(X = DEC_Embedding_5 ,perplexity= 150, is_distance = FALSE, check_duplicates = FALSE)

tsne_converted_food_DEC_5 <- tsne_converted_food_DEC_5$Y %>%
  data.frame() %>%
  setNames(c("X", "Y"))

tsne_converted_food_DEC_5$cl <- factor(resultft_DEL_all_5$cluster)
ggplot(tsne_converted_food_DEC_5, aes(x=X, y=Y, color=cl)) + geom_point()
```

```{r}
tsne_converted_food_DEC_3d_5 <- Rtsne(X = DEC_Embedding_5 ,perplexity= 150, dims = 3, is_distance = FALSE, check_duplicates = FALSE)

tsne_converted_food_DEC_3d_5 <- tsne_converted_food_DEC_3d_5$Y %>%
  data.frame() %>%
  setNames(c("X", "Y", "Z"))

tsne_converted_food_DEC_3d_5$cl <- factor(resultft_DEL_all_5$cluster)

p <- plot_ly(tsne_converted_food_DEC_3d_5, x = ~X, y = ~Y, z = ~Z, color = ~cl, colors = c('#BF382A', '#0C4B8E')) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = 'Dim1'),
                     yaxis = list(title = 'Dim2'),
                     zaxis = list(title = 'Dim3')))
p
```



```{r}
dist_plot_clust <-function(original_data, selected_variable){
  selected_variable <- enquo(selected_variable)
  ggplot(original_data, aes(UQ(selected_variable))) + geom_density(aes(fill = factor(cluster)), alpha=0.8) +
    labs(title = "Density plot",
         subtitle="sIgE_f1 of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="sIgE_f1",
         fill="# Clusters")
} 
```

```{r}
dist_plot_clust(original_data = resultft_DEL_all_5, selected_variable = age)
```


### Density plot shoiwing the age distribution for each cluster
```{r}
resultft_DEL_all$cluster <- as.factor(resultft_DEL_all_5$cluster)

age_g <- ggplot(resultft_DEL_all_5, aes(sIgE_f3))
age_p <- age_g + geom_density(aes(fill=factor(cluster)), alpha=0.8) +
    labs(title="Density plot",
         subtitle="sIgE_f1 of persons Grouped by Clusters",
         caption="Source: Source: results of Hierarchical clustering with tree-based distance and distance d1",
         x="sIgE_f1",
         fill="# Clusters")

ggplotly(age_p)
```


```{r}
g <- ggplot(resultft_DEL_all_5, aes(bmi2)) + scale_fill_brewer(palette = "Spectral")
s <- g + geom_histogram(aes(fill=factor(cluster)), 
                   bins=5, 
                   col="black", 
                   size=.1) +   # change number of bins
  labs(title="Histogram with Fixed Bins", 
       subtitle="Age across different clusters",
       x="Age",
         fill="# Clusters") 

ggplotly(s)
```

```{r}
table_uft_DEL_all <- tableby(cluster ~ ., data = as.list(resultft_DEL_all_5))
summary(table_uft_DEL_all, title = "Charachtaristcs of Clusters")
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
```



## Charachtiristic Analysis
```{r, warning=FALSE}
resultft_DEL_all_5$cluster <- as.factor(resultft_DEL_all_5$cluster)
catdes(resultft_DEL_all_5, 16)
```



# Random Data clusters with DEL (Deep Embedding Clustering layer)

```{r}
#result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_food_uft_DEL_k.csv")
result_rand_uft_DEL_k <- read.csv("/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_rand_uft_DEL_k.csv")
result_rand_uft_DEL_k$farmlive[result_rand_uft_DEL_k$farmlive == ""] <- NA
result_rand_uft_DEL_k <-  result_rand_uft_DEL_k %>% replace_na (list(farmlive = 'no'))
table_rand_uft_DEL_k <- tableby(cluster ~ ., data = as.list(result_rand_uft_DEL_k))
summary(table_rand_uft_DEL_k, title = "Charachtaristcs of Clusters")
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
```

## Charachtiristic Analysis
```{r, warning=FALSE}
result_rand_uft_DEL_k$cluster <- as.factor(result_rand_uft_DEL_k$cluster)
#result_food_uft_DEL_k <- result_food_uft_DEL_k[-c(1,2,20)]
catdes(result_rand_uft_DEL_k, 16)
```

# With asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)

```{r}
result_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_as_rh_uft_DEL.csv')
result_as_rh_uft_DEL$farmlive[result_as_rh_uft_DEL$farmlive == ""] <- NA
result_as_rh_uft_DEL <-  result_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_as_rh_uft_DEL))
summary(table_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
```

## Charachtiristic Analysis
```{r, warning=FALSE}
result_as_rh_uft_DEL$cluster <- as.factor(result_as_rh_uft_DEL$cluster)
catdes(result_as_rh_uft_DEL, 16)
```


# Without asthma and Rhinitis Data clusters with DEL (Deep Embedding Clustering layer)

```{r}
result_no_as_rh_uft_DEL <- read.csv('/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Intermediate/CsvData_Output/result_no_as_rh_uft_DEL.csv')
result_no_as_rh_uft_DEL$farmlive[result_no_as_rh_uft_DEL$farmlive == ""] <- NA
result_no_as_rh_uft_DEL <-  result_no_as_rh_uft_DEL %>% replace_na (list(farmlive = 'no'))
table_no_as_rh_uft_DEL <- tableby(cluster ~ ., data = as.list(result_no_as_rh_uft_DEL))
summary(table_no_as_rh_uft_DEL, title = "Charachtaristcs of Clusters")
# adding the id variable 
#result_food_uft_DEL_k$ID <- food_data_id$ID 
#write.csv(result_food_uft_DEL_k, "/Users/xbasra/Documents/Data/Clustering_Food_Alergies/CsvData/result_food_uft_DEL_k.csv")
```

## Charachtiristic Analysis
```{r, warning=FALSE}
result_no_as_rh_uft_DEL$cluster <- as.factor(result_no_as_rh_uft_DEL$cluster)
catdes(result_no_as_rh_uft_DEL, 16)
```




# adding the id varaible
```{r}
#resultft_DEL_all$ID <- food_data_id$ID
resultft_DEL_all_5$ID <- food_data_id$ID
result_rand_uft_DEL_k$ID <- rand_food_data_id$ID
result_as_rh_uft_DEL$ID <- as_ri_food_id$ID
result_no_as_rh_uft_DEL$ID <- no_as_ri_food_id$ID
write.csv(resultft_DEL_all_5,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/resultft_DEL_all_id_5.csv')
write.csv(result_rand_uft_DEL_k,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_rand_uft_DEL_k_id.csv')
write.csv(result_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_as_rh_uft_DEL_id.csv')
write.csv(result_no_as_rh_uft_DEL,'/Users/xbasra/Documents/Data/Clustering_Food_Alergies/Results/CSVData/result_no_as_rh_uft_DEL_id.csv')
```



















